DETAILED ACTION
Status of Claims
This action is in reply to the response received on 07 January 2026.
Claims 1, 9, and 15 have been amended.
Claims 1-20 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The Information Disclosure Statements filed on 10 October 2025 and 23 February 2026, have been considered. Initialed copy of the Forms 1449 is enclosed herewith.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea without significantly more).
Under step 1, it is determined whether the claims are directed to a statutory category of invention (see MPEP 2106.03(II)). In the instant case, claims 1-8 are directed to a product of manufacture (non-transitory computer-readable medium), claims 9-14 are directed to a method, and claims 15-20 are directed to a system.
While the claims fall within statutory categories, under revised Step 2A, Prong 1 of the eligibility analysis (MPEP 2106.04), the claimed invention recites an abstract idea of providing a user responses related to beauty topics. Specifically, representative claim 9 recites the abstract idea of:
receiving user input provided by a user via a user presented at a client;
obtaining contextual information for the user input;
transmitting the user input and the contextual information for the user input, wherein the transmitting comprises appending the contextual information to the user input in a prompt provided as input, and wherein the appended contextual information acts as a constraint on a response to the user input;
requesting to provide the confirmation that the user input relates to one or more beauty topics;
receiving the confirmation that the user input relates to the one or more beatify topics;
based on the confirmation, requesting to provide a response to the user input to be presented to a user, wherein the response relates to the one or more beauty topics and is based at least in part on the user input and the contextual information;
receiving the response; and
causing the response to be presented to the user.
Under revised Step 2A, Prong 1 of the eligibility analysis, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in 2106.04(a) of the MPEP. Even in consideration of the analysis, the claims recite an abstract idea. Representative claim 9 recites the abstract idea of providing a user responses related to beauty topics, as noted above. This concept is considered to be a method of organizing human activity. Certain methods of organizing human activity include “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” MPEP 2106.04(a)(2)(II). In this case, the abstract idea recited in representative claim 9 is a certain method of organizing human activity because it relates to sale activities since the claims specifically recites receiving user input by a user and presenting the input to a client, obtaining contextual information for the user input, transmitting the user input and the contextual information for the user input, wherein the transmitting comprises appending the contextual information to the user input in a prompt provided as input, and wherein the appended contextual information acts as a constraint on the response to the user input, requesting to provide confirmation that the user input relates to one or more beauty topics, receiving the confirmation that the user input relates to the one or more beauty topics, based on the confirmation, requesting to provide a response to the user input to be presented to a user, where the response relates to the one or more beauty topics and is base at least in part on the user input and the contextual information, receiving the response and causing the response to be presented to the user, thereby making this a sales activity or behavior.
Thus, representative claim 1 recites an abstract idea.
Under Step 2A, Prong 2 of the eligibility analysis, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. MPEP 2106.04(d). The courts have identified limitations that did not integrate a judicial exception into a practical application include limitations merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). MPEP 2106.04(d). In this case, representative claim 9 includes additional elements: a computer system, user interface, client computing device, a large language model (LLM), to the LLM, by the LLM, the LLM , from the LLM, and via the user interface.
Although reciting such additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 9 merely recites a commonplace business method (i.e., providing a user responses related to beauty topics) being applied on a general-purpose computer using general purpose computer technology. MPEP 2106.05(f). While the claims recite a large language model, the recitations are results based in nature and do not include details as to how the model is actually functioning beyond known functions. Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application.
Under Step 2B of the eligibility analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). MPEP 2106.05. In this case, as noted above, the additional elements of a computer system, user interface, client computing device, a large language model (LLM), to the LLM, by the LLM, the LLM , from the LLM, and via the user interface recited in independent claim 9 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘ad[d] nothing…that is not already present when the steps are considered separately’… [and] [v]iewed as a whole…[the] claims simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, (2014) (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, when viewed as a whole, representative claim 9 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in representative claim 9 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
As such, representative claim 9 is ineligible.
Independent claims 1 and 15 are similar in nature to representative claim 9 and Step 2A, Prong 1 analysis is the same as above for representative claim 9. It is noted that in independent claim 1 includes the additional elements of a non-transitory computer-readable medium having stored thereon instructions configured to, when executed by one or more computing devices of a computer system, cause the compute system to perform operations, and independent claim 15 includes the additional element of a computer system comprising a processor and a non-transitory computer-readable medium having stored thereon instructions. The Applicant’s specification does not provide any discussion or description of the claimed additional elements recited in claims 1 and 15 as being anything other than generic elements. Thus, the claimed additional elements of claims 1 and 15 are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. As such, the additional elements of claims 1 and 15 do not integrate the judicial exception into a practical application of the abstract idea. Additionally, the additional elements of claims 1 and 15, considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer.
As such, claims 1 and 15 are ineligible.
Dependent claims 2-8, 10-14, and 16-20, depending from claims 1, 9, and 15 respectively, do not aid in the eligibility of the independent claims and the representative claim 9. The claims of 2-8, 10-14, and 16-20 merely act to provide further limitations of the abstract idea and are ineligible subject matter.
It is noted that dependent claims include the additional elements of chat interface (claims 4, 11, & 18), interface element (claims 5, 7, 8, 12, 14, 19, & 20), digital model (claims 5, 6, 12, & 19), a camera and digital (claims 6, 13, & 19), and vector database (claim 17). Applicant’s specification does not provide any discussion or description of the claimed additional elements as being anything other than a generic element. The claimed additional elements, individually and in combination do not integrate into a practical application and do not provide an inventive concept because they are merely being used to apply the abstract idea using a generic computer (see MPEP 2106.05(f)). Accordingly, claims 4-8, 12-14, and 17-20 are directed towards an abstract idea. Additionally, the additional elements of claims 4-8, 12-14, and 17-20, considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer. It is further noted that the remaining dependent claims 2-3, 10-11, and 16 do not recite any further additional elements to consider in the analysis, and therefore would not provide additional elements that would integrate the abstract idea into a practical application and would not provide an inventive concept.
As such, claims 2-8, 10-14, and 16-20 are ineligible.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
nonobviousness.
Claims 1-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dissanayake, M. et al. (PGP No. US 2025/0166040 A1), in view of Petricek, V., et al. (Patent No. US 11,055,305 B1), and Ozcan, A., et al. (PGP No. US 2022/0414741 A1).
Claim 1-
Dissanayake discloses a non-transitory computer-readable medium having stored thereon instructions configured to, when executed by one or more computing devices of a computer system, cause the computer system to perform operations comprising (Dissanayake, see: paragraph [0016] “non-transitory computer-readable medium”):
receiving user input provided by a user via a user interface presented at a client computing device (Dissanayake, paragraph [0050] disclosing “responsive for receiving and responding to electronic requests”; and paragraph [0078] disclosing “AI-based method 250 comprises receiving, by the app, user-specific natural language data of a user (e.g., user 202a) [i.e., a client computing device]. The natural language data may be received, for example, from LLM interface 300”);
obtaining contextual information for the user input (Dissanayake, paragraph [0078] disclosing “data may be received” and “natural language data may define user-specific phenotype information and user-specific demographic data of the user, including, by way of non-limiting example, any one or more of gender, age, ethnicity, and/or skin concerns identifying skin issues or conditions”);
transmitting the user input and the contextual information for the user input to a large language model (LLM), wherein the transmitting comprises input to the LLM, and a response by the LLM (Dissanayake, paragraph [0050] disclosing “responsive for receiving and responding to electronic requests”; and paragraph [0079] disclosing “inputting, by the app (e.g., app 109 and/or 109a), the user-specific natural language data into the natural language model”);
requesting the LLM to provide that the user input relates to one or more beauty topics (Dissanayake, paragraph [0050] disclosing “responsive for receiving and responding to electronic requests”; [0079] disclosing “model may then generate, based on the natural language data, one or more user-specific phenotype classifications (e.g., skin health, skin appearance, skin dryness, skin shine, skin radiance) and one or more user-specific demographic classifications (e.g., gender, age, ethnicity, geographic area, health, and/or and other user information) defining the user”);
receiving the that the user input relates to the one or more beauty topics from the LLM (Dissanayake, [0079] disclosing “model may then generate, based on the natural language data, one or more user-specific phenotype classifications (e.g., skin health, skin appearance, skin dryness, skin shine, skin radiance) and one or more user-specific demographic classifications (e.g., gender, age, ethnicity, geographic area, health, and/or and other user information) defining the user”);
requesting the LLM to provide a response to the user input to be presented to a user, wherein the response relates to the one or more beauty topics and is based at least in part on the user input and the contextual information (Dissanayake, see: paragraph [0050] disclosing “responsive for receiving and responding to electronic requests”; and [0082] disclosing “inputting, by the app…the one or more phenotype classifications of the user, and the one or more demographic classifications of the user into the product recommendation model. The product recommendation model then generates a user-specific product recommendation for the user [i.e., response to the user input to be presented]. The user-specific product recommendation may include a product recommendation for a manufactured product (e.g., a skin creme or lotion having ingredients for treating a specific skin condition or issue)” and “recommendation may be designed to address at least one issue identified in the…user-specific phenotype classifications”);
receiving the response from the LLM (Dissanayake, see: paragraph [0050] disclosing “responsive for receiving and responding to electronic requests”; and [0082] disclosing “generates a user-specific product recommendation for the user [i.e., response to the user input to be presented]. The user-specific product recommendation may include a product recommendation for a manufactured product (e.g., a skin creme or lotion having ingredients for treating a specific skin condition or issue)”); and
causing the response to be presented to the user via the user interface (Dissanayake, see: paragraph [0084] disclosing “AI-based method 250 comprises outputting, by the app (e.g., app 109 and/or 109a), natural language data to the user describing the user-specific product recommendation” and “may include output from LLM interface 300, where the conversation engine of the natural language model converses with the user regarding the user-specific product recommendation, product usage, general advice regarding cause(s) of the user-specific skin condition or issue, and/or how to avoid the skin condition and/or issue in the future”; also see paragraph [0086]).
Although Dissanayake does disclose a user input that is transmitted and input into a large language model, and provides a response via the LLM, Dissanayake does not disclose or describe transmitting includes appending the contextual information to the user input in a prompt as provided by the model, where the contextual information acts as a constraint on a response to the model. Dissanayake does not disclose:
wherein the transmitting comprises appending the contextual information to the user input in a prompt provided as input, and wherein the appended contextual information acts as a constraint on a response;
Petricek, however, does teach:
wherein the transmitting comprises appending the contextual information to the user input in a prompt provided as input, and wherein the appended contextual information acts as a constraint on a response (Petricek, see: Col. 9, ln. 4-16 teaching “user interface 200 presented via the user device after the men’s department filter 224a has been selected in the chat view” and “chat window 234 has been updated” and “a record of the user’s selection of the mend’s department filter 244a is represented by user comment 248a” and “refinement bot 242 has responded by suggested additional filters 250 (e.g., ‘some more ways you can explore’)” and “filters include a rating filter 250a that limits [i.e., constraint on a response] the search results to items rating four stars and up”; and see: ln. 38-44 teaching “the content in the chat window 234 has been updated. For example, a record of the user's selection of the rating filter 250 is represented by the user comment 248b. Following the user comment 248b, the refinement bot 242 has responded by presenting a set of recommended items 252 and presenting a set of recommended search filters 254 [i.e., appended contextual information acts as a constraint on a response]”; Also see FIG. 6 and FIG. 7, depicting the user input as a prompt, causing the system to append the contextual information as a further constraint on a response.).
This step of Petricek is applicable to the product of manufacture of Dissanayake, as they both share characteristics and capabilities, namely, they are directed to providing relevant recommendations to a user based on interactions with the user. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the product of manufacture of Dissanayake, to also include the features of wherein the transmitting comprises appending the contextual information to the user input in a prompt provided as input, and wherein the appended contextual information acts as a constraint on a response, as taught by Petricek. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Dissanayake, to improve the relevancy of search results of a user by narrowing the user search and provide better product recommendations (Petricek, see: Col. 2, ln. 49-64).
Although Dissanayake discloses that the user inputs are received by the system, where the inputs are related to one or more beauty topics, Dissanayake does not specifically state that there is a receiving of a confirmation that the user input relates to a topic.
Dissanayake does not disclose:
provide a confirmation that the user input relates to topics,
receiving the confirmation that the user input relates to topics,
based on the confirmation, a response
Ozcan, however, does teach:
provide a confirmation that the user input relates to topics (Ozcan, see: paragraph [0180] teaching “question may be attached to a label and may have different candidate answers related to that label” and “may be a binary question (yes/no) that confirms whether the user should be assigned a label or not” and “the question may be ‘Are you concerned with wrinkles?’” and “if the user answer is yes, then the ‘wrinkles’ label may be added to the user’s profile”; and paragraph [0182] teaching “conversation relates to a product confirmation, such as when a user has asked to confirm that a specific product will be suitable for the user’s requirements”),
receiving the confirmation that the user input relates to topics (Ozcan, see: pargraph [0180] teaching “may be a binary question (yes/no) that confirms whether the user should be assigned a label or not” and “the question may be ‘Are you concerned with wrinkles?’” and “if the user answer is yes, then the ‘wrinkles’ label may be added to the user’s profile”),
based on the confirmation, a response (Ozcan, see: paragraph [0185] teaching “If the topic is a specific product identifier, then only the questions and dialog turns that are relevant to confirm whether that given product is recommendable for the user may be selected”).
This step of Ozcan is applicable to the product of manufacture of Dissanayake, as they both share characteristics and capabilities, namely, they are directed to personalized recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the product of manufacture of Dissanayake, to also include the features of provide a confirmation that the user input relates to topics, receiving the confirmation that the user input relates to topics, and based on the confirmation, a response, as taught by Ozcan. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Dissanayake, to improve the quality of machine learning algorithms to provide relevant content and recommendations of products to user that they are likely to purchase (Ozcan, see: paragraphs [0145] and [0173]).
Claim 2-
Dissanayake in view of Petricek, and Ozcan teach the computer-readable medium of Claim 1, as described above.
Dissanayake discloses wherein requesting the LLM to provide that the user input relates to one or more beauty topics comprises:
requesting the LLM to provide a first classification of the user input (Dissanayake, see: paragraph [0008] disclosing “where the AI model communicates with a user to ask questions and receive information”; and see: [0039] disclosing “his or her specific concerns and other information (e.g., demographic and/or phenotype information) through a conversation”; paragraph [0044] disclosing “AI-based system and method for identification and classification of such skin conditions, issues, or concerns”; and paragraph [0065] disclosing “one or more phenotype classifications…of the respective users as input”);
receiving the first classification of the user input from the LLM, wherein the first classification indicates that the user input relates to the one or more beauty topics (Dissanayake, see: paragraph [0039] disclosing “a user may have a specific skin concern [i.e., first classification]. The user may describe or otherwise provide, through natural language, to the AI-based systems and methods, his or her specific concerns and other information (e.g., demographic and/or phenotype information) through a conversation”); and
responsive to the first classification indicating that the user input relates to the one or more beauty topics, requesting the LLM to provide a second classification of the user input that further defines the one or more beauty topics, wherein the confirmation comprises the first classification and the second classification (Dissanayake, see: [0040] disclosing “may identify wrinkles” and “Through a conversation, the user can describe these features to the AI-based systems and methods, where the AI-based systems and methods identify and recommend a wrinkle-related skin care product”).
Dissanayake does not disclose:
a confirmation of the user input,
Ozcan teaches:
a confirmation of the user input (Ozcan, see: pargraph [0180] teaching “may be a binary question (yes/no) that confirms whether the user should be assigned a label or not” and “the question may be ‘Are you concerned with wrinkles?’” and “if the user answer is yes, then the ‘wrinkles’ label may be added to the user’s profile”),
This step of Ozcan is applicable to the product of manufacture of Dissanayake, as they both share characteristics and capabilities, namely, they are directed to personalized recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the product of manufacture of Dissanayake, to also include the features of a confirmation of the user input, as taught by Ozcan. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Dissanayake, to improve the quality of machine learning algorithms to provide relevant content and recommendations of products to user that they are likely to purchase (Ozcan, see: paragraphs [0145] and [0173]).
Claim 3-
Dissanayake in view of Petricek, and Ozcan teach the computer-readable medium of Claim 1, as described above.
Dissanayake discloses:
wherein the user input includes text input or voice input (Dissanayake, see: paragraph [0008] disclosing “Natural language data (e.g., text and/or voice data of the user) may be received from the user”).
Claim 4-
Dissanayake in view of Petricek, and Ozcan teach the computer-readable medium of Claim 1, as described above.
Dissanayake discloses:
wherein the user interface comprises a chat interface, and wherein the response comprises text presented to the user in the chat interface (Dissanayake, see: paragraph [0125] disclosing “may cause user interface 602 to prompt the user to initiate a chat, e.g., such as launching or displaying LLM interface 30”; Also see FIG. 3 of the chat interface with initiation of text in the conversation.).
Claim 5-
Dissanayake in view of Petricek, and Ozcan teach the computer-readable medium of Claim 1, as described above.
Dissanayake discloses:
wherein the user interface comprises a skin analysis request user interface element (Dissanayake, see: paragraph [0057] disclosing “(e.g., AI models 108) and/or imaging analysis”), the operations further comprising:
receiving an indication of activation of the skin analysis request user interface element (Dissanayake, see: paragraph [0057] disclosing “may be transmitted via computer network 120 to server(s) 102 for training of model(s) (e.g., AI models 108) and/or imaging analysis” and paragraph [0064] disclosing “images may be tagged or labeled, for example, with meta data, defining phenotype and/or demographic information of the respective individuals” and “defining or identifying locations of skin issues or conditions (e.g., hemoglobin and/or melanin) identified in a given image depicting a user's skin”); and
responsive to the indication of activation of the skin analysis request user interface element (Dissanayake, see: [0064] disclosing “images may be tagged or labeled, for example, with meta data, defining phenotype and/or demographic information of the respective individuals”):
obtaining a digital model of a face of the user (Dissanayake, see: paragraph [0065] disclosing “the digital twin images may be generated to have skin issues or conditions (e.g., pigmented spots, wrinkles and/or fine lines, acne, pores, sagging and/or loss of elasticity, uneven texture, skin thinning, dryness, oiliness, sensitivity, uneven skin tone, eczema, dermatitis)”);
requesting the LLM to generate a product recommendation or a care routine recommendation based at least in part on the digital model of the face (Dissanayake, see: pargraph [0068] disclosing “product recommendation model on the digital twin images of the respective users as output” and product recommendations for the respective users based on the digital twin images, the one or more phenotype classifications” and “recommendation can be for a skin care product specific to the user” and “correlated to treating a known skin care condition or issue (e.g., pigmented spots, wrinkles and/or fine lines, acne, pores, sagging, and/or loss of elasticity, uneven texture, skin thinning, dryness, oiliness, sensitivity, uneven skin tone, eczema, dermatitis)”).
Claim 6-
Dissanayake in view of Petricek, and Ozcan teach the computer-readable medium of Claim 5, as described above.
Dissanayake discloses wherein the client computing device comprises a camera (paragraph [0092] disclosing “a digital camera”), the operations further comprising:
causing the client computing device to request activation of the camera to capture one or more digital images (Dissanayake, see: paragraph [0092] disclosing “Each of the digital twin images may comprise photorealistic images comprising pixel data, for example, as would have been captured by a digital camera”);
receiving the one or more captured digital images (Dissanayake, pargraph [0092] disclosing “Digital twin image 202a_dt1 (as well as the digital twin images depicted for FIG. 4A, including, by way of non-limiting example 202b_dt1 and 202c_dt1) may be generated based on user-specific phenotype classifications and user-specific demographic classifications as received from or determined” and “; and
generating the digital model of the face of the user based at least in part on the one or more captured digital images (Dissanayake, paragraph [0066] disclosing “configured to produce photo realistic facial images”; and see: [0095] disclosing “generate a user-specific digital twin image (e.g., digital twin image 202a_dt) having the same or similar skin condition or issue (e.g., melanin related spot(s), such as melanin related spot 202ar3) in the same or similar skin area (e.g., skin area 202ar)”).
Claim 7-
Dissanayake in view of Petricek, and Ozcan teach the computer-readable medium of Claim 1, as described above.
Dissanayake discloses:
wherein the user interface comprises a product information user interface element configured to allow the user to view information corresponding to recommended products, the operations further comprising (Dissanayake, see: paragraph [0057] disclosing “data may be received from an application implementing a GPT interface” and paragraphs [0118]-[0119]; Also see FIG. 6 showing the interface with several elements allowing the user to choose to input a specific skin issue, and can select the product recommendations element, and further the interface displays the recommendations at the bottom of the display (el. 622)):
requesting the LLM to generate one or more product recommendations (Dissanayake, see: paragraph [0118] disclosing “User interface 602 may also include or render a user-specific product recommendation 612”, Also see FIG. 6 displaying the requested product recommendations for a specific skin issue.);
receiving the one or more product recommendations from the LLM (Dissanayake, see: paragraph [0118] disclosing “include or render a user-specific product recommendation 612”); and
causing the client computing device to present the one or more product recommendations to the user via the product information user interface element (Dissanayake, see: [0118] disclosing “display screen of a computing device, at least one user-specific product recommendation based on the user-specific prediction and/or simulated image” and paragraph [0119] disclosing “include or render a section for a specific product recommendation 622 for a manufactured product 624r (e.g., night face cream as described above). The product recommendation 622 may correspond to the user-specific product recommendation 612”; Also see: FIG. 6).
Claim 8-
Dissanayake in view of Petricek, and Ozcan teach the computer-readable medium of Claim 1, as described above.
Dissanayake discloses wherein the user interface comprises a content selection user interface element configured to allow the user to view information corresponding to recommended content (FIG. 6 showing the interface with several elements allowing the user to choose to input a specific skin issue, and can select the product recommendations element, and further the interface displays the recommendations at the bottom of the display (el. 622)), the operations further comprising:
requesting the LLM to generate one or more content recommendations (Dissanayake, see: paragraph [0118] disclosing “User interface 602 may also include or render a user-specific product recommendation 612”, Also see FIG. 6 displaying the requested product recommendations for a specific skin issue.);
receiving the one or more content recommendations from the LLM (Dissanayake, see: paragraph [0118] disclosing “include or render a user-specific product recommendation 612”); and
causing the client computing device to present the one or more content recommendations to the user via the content selection user interface element (Dissanayake, see: [0118] disclosing “display screen of a computing device, at least one user-specific product recommendation based on the user-specific prediction and/or simulated image” and paragraph [0119] disclosing “include or render a section for a specific product recommendation 622 for a manufactured product 624r (e.g., night face cream as described above). The product recommendation 622 may correspond to the user-specific product recommendation 612”; Also see: FIG. 6).
Regarding claim 9, claim 9 is directed to a method. Claim 9 recites limitations that are parallel in nature to those addressed above for claim 1 which is directed towards a product of manufacture. Claim 9 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 10, claim 10 is directed to a method. Claim 10 recites limitations that are parallel in nature to those addressed above for claim 2 which is directed towards a product of manufacture. Claim 10 is therefore rejected for the same reasons as set forth above for claim 2.
Regarding claim 11, claim 11 is directed to a method. Claim 11 recites limitations that are parallel in nature to those addressed above for claim 4 which is directed towards a product of manufacture. Claim 11 is therefore rejected for the same reasons as set forth above for claim 4.
Regarding claim 12, claim 12 is directed to a method. Claim 12 recites limitations that are parallel in nature to those addressed above for claim 5 which is directed towards a product of manufacture. Claim 12 is therefore rejected for the same reasons as set forth above for claim 5.
Regarding claim 13, claim 13 is directed to a method. Claim 13 recites limitations that are parallel in nature to those addressed above for claim 6 which is directed towards a product of manufacture. Claim 13 is therefore rejected for the same reasons as set forth above for claim 6.
Regarding claim 14, claim 14 is directed to a method. Claim 14 recites limitations that are parallel in nature to those addressed above for claim 7 which is directed towards a product of manufacture. Claim 14 is therefore rejected for the same reasons as set forth above for claim 7.
Regarding claim 15, claim 15 is directed to a system. Claim 15 recites limitations that are similar in nature to those addressed above for claim 1 which is directed towards a product of manufacture. It is noted that Dissanayake discloses the features of a computer system comprising a processor and a non-transitory computer-readable medium having stored thereon instructions configured to, when executed by one or more computing devices of a computer system, cause the computer system to perform operations (Dissanayake, see: paragraph [0016] disclosing “a tangible, non-transitory computer-readable medium storing instructions for providing personalized skin product recommendations is disclosed. The instructions, when executed by one or more processors, may cause the one or more processors to implement”). Claim 15 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 16, claim 16 is directed to a system. Claim 16 recites limitations that are parallel in nature to those addressed above for claim 2 which is directed towards a product of manufacture. Claim 16 is therefore rejected for the same reasons as set forth above for claim 2.
Regarding claim 18, claim 18 is directed to a system. Claim 18 recites limitations that are parallel in nature to those addressed above for claim 4 which is directed towards a product of manufacture. Claim 18 is therefore rejected for the same reasons as set forth above for claim 4.
Regarding claim 19, claim 19 is directed to a system. Claim 19 recites limitations that are parallel in nature to those addressed above for claims 5 and 6, which are directed towards a product of manufacture. Claim 19 is therefore rejected for the same reasons as set forth above for claims 5 and 6.
Regarding claim 20, claim 20 is directed to a system. Claim 20 recites limitations that are parallel in nature to those addressed above for claims 7 and 8, which are directed towards a product of manufacture. Claim 20 is therefore rejected for the same reasons as set forth above for claims 7 and 8.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Dissanayake, M. et al., in view of Petricek, V., et al., Ozcan, A., et al., and Lindgren, S. (PGP No. US 2023/0385903 A1).
Claim 17-
Dissanayake in view of Petricek, and Ozcan teach the computer system of Claim 15, as described above.
Dissanayake discloses the operations further comprising:
requesting the LLM to provide a summary of the user input (Dissanayake, see: paragraph [0090] disclosing “LLM interface 300 comprises a text-based interface, e.g., a conversation engine, in which a user (e.g., user 202a) can interact with the artificial intelligence (AI)-based system 100 of FIG. 1. LLM interface 300 can also be configured to provide personalized skin product recommendations” Also see: FIG. 3 rendering the summary of the user input that had previously been input, describing the topic the user is interested in.);
receiving the summary of the user input from the LLM (Dissanayake, see: paragraph [0091] disclosing “user interacts with LLM interface 300 by providing natural language text-based data indicating a skin type and indicating a skin condition or issue. The user further asks a natural language question regarding ingredients of a product used to treat the skin condition or issue. The user's natural language data can be received by an application (e.g., app 109a and/or app 109) and then provided or transferred to a GPT AI model (e.g., a GPT-4 AI model instance stored on server(s) 102). The GPT AI model can respond with output providing relevant information, and also seek new natural language data from the user in order to gain a complete set of data for providing to a natural language model”);
generating a vector of the user input (Dissanayake, see: paragraph [0072] disclosing “machine learning program or algorithm may employ a neural network” and “learns in two or more features or feature datasets (e.g., natural language data and/or pixel data) in a particular areas of interest” and “The machine learning programs or algorithms may also include…support vector machine (SVM) analysis”);
comparing the vector of the user input in a database (Dissanayake, see: paragraph [0072 disclosing “K-Nearest neighbor analysis” and “Machine learning may involve identifying and recognizing patterns in existing data”); and
a near-neighbor for the vector of the user input among the other representations in the database (Dissanayake, see: paragraph [0072 disclosing “K-Nearest neighbor analysis” and “Machine learning may involve identifying and recognizing patterns in existing data”).
Dissanayake does not disclose:
a vector representation;
the vector representation;
other vector representations;
identifying a match for the vector representation among other representations in the vector;
Lindgren, however, does teach:
a vector representation (Lindgren, see: paragraph [0042] teaching “to create a requirement vector, and which uses the requirement vector as an input into one or more machine and deep learning algorithms to generate as output personalized beauty product recommendations” and pargraph [0058] teaching “database(s) 150 has a unique vector representation”);
the vector representation (Lindgren, see: paragraph [0042] teaching “to create a requirement vector, and which uses the requirement vector as an input into one or more machine deep learning algorithms to generate as output personalized beauty product recommendations” and paragraph [0058] teaching “database(s) 150 has a unique vector representation”);
other vector representations (Lindgren, see: paragraph [0060] teaching “vector representations are processed by the hidden layers for feature extraction”).;
identifying a match for the vector representation among other representations in the vector (Lindgren, see: paragraph [0059] teaching “Using all user information and the environmental factors a requirement vector is created 162” and “similarity score of products may be calculated between all product vectors and the requirement vector” and “system 100 can match customer requirements to products”).
This step of Lindgren is applicable to the system of Dissanayake as they both share characteristics and capabilities, namely, they are directed to providing personalized recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Dissanayake, to also include the features of a vector representation, the vector representation, other vector representations, and identifying a match for the vector representation among other representations in the vector, as taught by Lindgren. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dissanayake, to improve predictions of recommendations of products to a customer (Lindgren, see: paragraph [0060]).
Response to Arguments
With respect to the rejections made under 35 USC § 101, the Applicant’s arguments filed on 07 January 2026, have been fully considered but are not considered persuasive.
In response to the Applicant’s arguments found on pages 10-11 of the remarks stating that “the amended claims include a technical improvement that improves the performance of the system” and “the pending claims are eligible at least under Step 2A, Prong Two of the Alice framework because the claims reflect an improvement to the functioning of the computer system in this context, thereby integrating the alleged judicial exception into a practical application,” the Examiner respectfully disagrees. As indicated in the office action above, the claims recite and are directed to an abstract idea under Step 2A, Prong One of the eligibility analysis. Next, even when considering the amendments to the claims, the claims do not integrate the abstract idea into a practical application. The additional elements were analyzed and are still considered to be recited in a generic manner and are being used to apply the abstract idea with a generically recited computer and computing components. The additional elements of a computer system, user interface, client computing device, a large language model (LLM), to the LLM, by the LLM, the LLM , from the LLM, and via the user interface recited in the amended claims, are still recited at a high-level of generality and are not sufficient to integrate the abstract idea into a practical application. Further, the claims do not reflect any type of improvement to the technology. The MPEP (2106.05(a)) provides further guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, as indicated in 2106.05(d)(1) of the MPEP “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement,” and that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art.” Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
In this case, Applicant’s specification provides no explanation of an improvement to the functioning of a computer or other technology. Rather, the claims focus “on a process that qualifies as an ‘abstract idea’ for which computers are invoked merely as a tool”. Id citing Enfish at 1327, 1336. Although the claims include computer technology such as a computer system, user interface, client computing device, a large language model (LLM), to the LLM, by the LLM, the LLM , from the LLM, and via the user interface, such elements are merely peripherally incorporated in order to implement the abstract idea. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to improving the existing technological process but are directed to improving the commercial task of providing a user responses related to beauty topics. The claimed process, while arguably resulting in improved responses provided to the user that are related to beauty topics, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the processor and/or computer components that operate the system. Rather, the claimed process is utilizing different data while still employing the same processor and/or computer components used in conventional systems to improve providing user responses related to beauty topics, e.g. commercial process. As such, the claims do not recite specific technological improvements, do not integrate the abstract idea into a practical application, and thus, the Examiner maintains the 101 rejection.
With respect to the rejections made under 35 U.S.C. 103, Applicant’s arguments filed on 07 January 2026 have been considered and are persuasive. However, in view of the amendments, Applicant’s arguments are moot and new grounds of rejection have been applied. The Examiner is relying on the reference of Petricek to teach the amended limitations. The new grounds of rejection have been necessitated by Applicant’s amendments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kim, M. (PGP No. US 2021/0279232 A1), describes a search system that includes a chatbot information collection unit collects log information recorded by associating exchange of a text with a terminal device with date and time information from a chatbot server device that provides a chat service by automatically generating a response text and transmitting the response text to the terminal device. An evaluation and measurement unit generates evaluation information of the chatbot server device to write the evaluation information to an evaluation information storage unit on the basis of the evaluation information storage unit configured to store the evaluation information of the chatbot server device and the log information. A search unit reads the evaluation information of the chatbot server device matching a search condition from the evaluation information storage unit and outputs information of the chatbot.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEY PRESTON whose telephone number is (571)272-4399. The examiner can normally be reached M-F 9-5.
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/ASHLEY D PRESTON/Primary Examiner, Art Unit 3688